Furthermore, to make sure that the results can potentially be extended to other texts, we applied the trained classifier to all lemmas from the human-annotated Universal Dependencies English Treebank [1]}. The words not present in the distributional model were omitted (they sum to 27% of word types and 10% of word tokens). The classifier showed an F-Score equal to 0.99, further demonstrating the robustness of the classifier. Note, however, that part of this performance is because the UD Treebank contains many words from the classifier training set. Essentially, it means that the decisions of the UD human annotators are highly consistent with the distributional patterns of words in the BNC.
